tml-epfl / sharpness-vs-generalization
A modern look at the relationship between sharpness and generalization [ICML 2023]
☆43Updated last year
Alternatives and similar repositories for sharpness-vs-generalization:
Users that are interested in sharpness-vs-generalization are comparing it to the libraries listed below
- Towards Understanding Sharpness-Aware Minimization [ICML 2022]☆35Updated 2 years ago
- ☆34Updated 11 months ago
- Distilling Model Failures as Directions in Latent Space☆46Updated last year
- Implementation of Confidence-Calibrated Adversarial Training (CCAT).☆45Updated 4 years ago
- Official code for "In Search of Robust Measures of Generalization" (NeurIPS 2020)☆28Updated 4 years ago
- ☆55Updated 4 years ago
- Code for the paper "The Journey, Not the Destination: How Data Guides Diffusion Models"☆19Updated last year
- Source code of "What can linearized neural networks actually say about generalization?☆19Updated 3 years ago
- ☆27Updated 6 months ago
- Code for the ICLR 2022 paper. Salient Imagenet: How to discover spurious features in deep learning?☆36Updated 2 years ago
- On the effectiveness of adversarial training against common corruptions [UAI 2022]☆30Updated 2 years ago
- ☆17Updated 2 years ago
- Code relative to "Adversarial robustness against multiple and single $l_p$-threat models via quick fine-tuning of robust classifiers"☆18Updated 2 years ago
- Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)☆13Updated 2 years ago
- ☆38Updated 3 years ago
- Training vision models with full-batch gradient descent and regularization☆38Updated last year
- An Investigation of Why Overparameterization Exacerbates Spurious Correlations☆30Updated 4 years ago
- Official repo for the paper "Make Some Noise: Reliable and Efficient Single-Step Adversarial Training" (https://arxiv.org/abs/2202.01181)☆25Updated 2 years ago
- ☆43Updated 2 years ago
- [NeurIPS'22] Official Repository for Characterizing Datapoints via Second-Split Forgetting☆14Updated last year
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆41Updated last year
- Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet☆29Updated last year
- A simple and efficient baseline for data attribution☆11Updated last year
- Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians (ICML 2019)☆17Updated 5 years ago
- Sharpness-Aware Minimization Leads to Low-Rank Features [NeurIPS 2023]☆25Updated last year
- ☆40Updated 2 years ago
- Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation☆44Updated last year
- The Pitfalls of Simplicity Bias in Neural Networks [NeurIPS 2020] (http://arxiv.org/abs/2006.07710v2)☆39Updated 11 months ago
- Provably (and non-vacuously) bounding test error of deep neural networks under distribution shift with unlabeled test data.☆9Updated 10 months ago
- [ICLR'22] Self-supervised learning optimally robust representations for domain shift.☆23Updated 2 years ago